Abstract
<p class="0abstract">Medical image retrieval (MIR) is a hard task owing to the varied patterns and structures in the medical images. The feature descriptors have been used to describe the images in most MIR approaches. Based on the local relationship, several feature descriptors of neighbouring image pixels have been proposed for MIR so far, but their low performance scores make them unsuitable. In this paper, an efficient optimized hybrid local lifting wavelet co-occurrence texture pattern for content-based MIR is proposed. Initially, image resize and Adaptive histogram equalization technique is used to carried out for contrast enhancement. Then Local Lifting Wavelet Co-occurrence Texture Pattern is derived using Local tetra pattern, Gradient directional pattern, lifting wavelet transform and Gray level co-occurrence matrix. An Equilibrium optimization technique is employed to select the most important features of an image from the obtained feature vectors (FV). Finally, to match the query image with the database images, distance between their FV is computed and the minimum distance images are considered as retrieval outcome. Three benchmark medical databases of various modalities (CT and MRI) are used to test the efficiency of the proposed method: EXACT-09, TCIA-CT, and OASIS. The experimental results prove that the proposed approach outperforms existing descriptors in terms of APR and ARR.</p>
Highlights
Computer assisted medical image analysis techniques help the medical doctors to make better their ability in disease judgment [1,2]
The Fvalue is computed based on the average precision rate (APR) and average recall rate (ARR) values as in eqn (42)
The results are compared with the existing feature descriptors such as local binary pattern (LBP) [4], local ternary pattern (LTP) [32], Local derivative pattern (LDP) [33], local tetra pattern (LTrP) [7], Local ternary co-occurrence patterns (LTCoPs) [8], LMeP [34], SS-3D-LTP [35], Local wavelet pattern (LWP) [12], Histogram of Compressed Scattering Coefficients (HCSC) [36], Directional binary wavelet pattern (DBWP) [6], Directional local ternary quantized extrema Pattern (DLTe-rQEP) [38], Local neighboring binary pattern (LNBP) [39], Fast discrete curvelet transform (FDCT) [40] and Local directional frequency encoded pattern (LDFEP) [37]
Summary
Computer assisted medical image analysis techniques help the medical doctors to make better their ability in disease judgment [1,2]. The medical images are of various modalities such as X-ray, computer tomography (CT) and etc. Due to the large size of medical repositories and different modalities, image search or retrieval from the databases for a given query image has become a difficult task. Content based medical image retrieval (CBMIR) was presented to solve these issues [3]. As manual search by the medical experts and text-based image search (TBIS). In CBMIR, Image search can be done by their visual contents. The contents represent the characteristics of the image such as shape, colour, textures, edges, visual features and etc. In CBMIR, feature representation plays a crucial role to determine the image similarity. Due to its enormous popularity and simplicity, several LBP variants have been proposed for solving image retrieval problems in recent decades [5]. Section - 2 provides the literature survey; section - 3 gives the explanation of proposed method; section - 4 provides the experimental results; and section-5 concludes
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